Bi-Level Game Strategy for Virtual Power Plants Based on an Improved Reinforcement Learning Algorithm - Zhu Liu; Guowei Guo; Dehuang Gong; Lingfeng Xuan; Feiwu He; Xinglin Wan; Dongguo Zhou (Energies)
To address the issue of economic dispatch imbalance in virtual power plant (VPP) systems caused by the influence of operators and distribution networks, this study introduces an optimized economic dispatch method based on bi-level game theory. Firstly, a bi-level game model is formulated, which integrates the operational and environmental expenses of VPPs with the revenues of system operators. To avoid local optima during the search process, an enhanced reinforcement learning algorithm is developed to achieve rapid convergence and obtain the optimal solution. Finally, case analyses illustrate that the proposed method effectively accomplishes multi-objective optimization for various decision-making stakeholders, including VPP and system operators, while significantly reducing curtailment costs associated with the extensive integration of distributed renewable energy. Furthermore, the proposed algorithm achieves fast iteration and yields superior dispatch outcomes under the same modeling conditions.